Introduction

vedacls is an open source classification toolbox based on PyTorch.

License

This project is released under the Apache 2.0 license.

Installation

Requirements

• Linux
• Python 3.6+
• PyTorch 1.4.0 or higher
• CUDA 9.0 or higher

We have tested the following versions of OS and softwares:

• OS: Ubuntu 16.04.6 LTS
• CUDA: 10.2
• PyTorch 1.4.0
• Python 3.6.9

Install vedacls

1. Create a conda virtual environment and activate it.

conda create -n vedacls python=3.6.9 -y
conda activate vedacls

2. Install PyTorch and torchvision following the official instructions, e.g.,

conda install pytorch torchvision -c pytorch

3. Clone the vedacls repository.

git clone https://github.com/Media-Smart/vedacls.git
cd vedacls
vedacls_root=${PWD}

4. Install dependencies.

pip install -r requirements.txt

Prepare data

The catalogue structure of dataset supported by vedacls toolbox is as follows:

data/
├── train
│   ├── 0
│   │   ├── XXX.jpg
│   │     
│   ├── 1
│   ├── 2
│   ├── ...
│
├── val
│   ├── 0
│   ├── 1
│   ├── 2
│   ├── ...
│ 
├── test
    ├── 0
    ├── 1
    ├── 2
    ├── ...

Train

1. Config

Modify some configuration accordingly in the config file like configs/resnet18.py

2. Run

python tools/train.py configs/resnet18.py

Snapshots and logs will be generated at ${vedacls_root}/workdir/resnet18

Test

1. Config

Modify some configuration accordingly in the config file like configs/resnet18.py

2. Run

python tools/test.py configs/resnet18.py checkpoint_path

Inference

1. Config

Modify some configuration accordingly in the config file like configs/resnet18.py

2. Run

python tools/inference.py configs/resnet18.py checkpoint_path image_path

Deploy

1. Install volksdep following the official instructions

2. Benchmark(optional)

python tools/deploy/benchmark.py configs/resnet18.py checkpoint_path image_path

More available arguments are detailed in tools/deploy/benchmark.py

3. Export model as ONNX or TensorRT engine format

python tools/deploy/export.py configs/resnet18.py checkpoint_path image_path out_model_path

More available arguments are detailed in tools/deploy/export.py

4. Inference SDK

You can refer to FlexInfer for details.

Contact

This repository is currently maintained by Chenhao Wang (@C-H-Wong), Hongxiang Cai (@hxcai), Yichao Xiong (@mileistone).

Credits

We got a lot of code from mmcv and mmdetection, thanks to open-mmlab.